Evaluation of a Predictive Low-Glucose Management System In-Clinic

A sensor-augmented pump with a predictive low-glucose suspend system could lead to an optimal time in target range during pregnancy in Japanese women with type 1 diabetes

Introduction

It is challenging for pregnant women with type 1 diabetes to maintain optimum glucose level to attain good neonatal outcomes. This study evaluated the efficacy of sensor-augmented insulin pump (SAP) with a predictive low-glucose suspend (PLGS) system in pregnant Japanese women with type 1 diabetes.

Materials and methods

SAP with PLGS was used in 11 of the 22 women with type 1 diabetes who delivered between 2011 and 2021 at the two medical institutions in Japan. Glucose management, insulin delivery suspension time (IST) and neonatal outcomes were retrospectively studied.

Results

In SAP with PLGS cases (n = 11), average glycated hemoglobin levels were < 6.5% throughout the pregnancy, and the time in range (TIR, 63-140 mg/dl) was > 70% in the second and third trimesters. PLGS was safely used without inducing ketoacidosis. Positive correlation was observed between IST and TIR (r = 0.62, p < 0.01). Negative correlation was observed between IST and time below range (TBR) (r =  - 0.40, p = 0.02), and IST and time above range (TAR) (r =  - 0.45, p = 0.01). Total daily insulin dose was adequately increased without increasing hypoglycemia. There was only one heavy-for-date HFD) infant among the 11 newborns in SAP with PLGS cases. In cases without SAP (n = 11), target glycemic levels were difficult to achieve and there were 5 HFD infants among the 11 newborns.

Conclusion

SAP with PLGS was safely and effectively used in pregnant women with type 1 diabetes to achieve target glucose levels without increasing the risk of hypoglycemia, which may have led to good neonatal outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13340-024-00716-7.

14. Children and Adolescents: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations

BACKGROUND

The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual's longer term control.

METHODS

We introduce explainable machine learning to make predictions of hypoglycemia (<70 mg/dL) and hyperglycemia (>270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user.

RESULTS

Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model.

CONCLUSIONS

Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user's glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications.

Management of type 1 diabetes in pregnancy: update on lifestyle, pharmacological treatment, and novel technologies for achieving glycaemic targets

Glucose concentrations within target, appropriate gestational weight gain, adequate lifestyle, and, if necessary, antihypertensive treatment and low-dose aspirin reduces the risk of pre-eclampsia, preterm delivery, and other adverse pregnancy and neonatal outcomes in pregnancies complicated by type 1 diabetes. Despite the increasing use of diabetes technology (ie, continuous glucose monitoring and insulin pumps), the target of more than 70% time in range in pregnancy (TIRp 3·5-7·8 mmol/L) is often reached only in the final weeks of pregnancy, which is too late for beneficial effects on pregnancy outcomes. Hybrid closed-loop (HCL) insulin delivery systems are emerging as promising treatment options in pregnancy. In this Review, we discuss the latest evidence on pre-pregnancy care, management of diabetes-related complications, lifestyle recommendations, gestational weight gain, antihypertensive treatment, aspirin prophylaxis, and the use of novel technologies for achieving and maintaining glycaemic targets during pregnancy in women with type 1 diabetes. In addition, the importance of effective clinical and psychosocial support for pregnant women with type 1 diabetes is also highlighted. We also discuss the contemporary studies examining HCL systems in type 1 diabetes during pregnancies.

Emerging Diabetes Technologies: Continuous Glucose Monitors/Artificial Pancreases

Over the past decade there have been many advances in diabetes technologies, such as continuous glucose monitors (CGM s), insulin-delivery devices, and hybrid closed loop systems . Now most CGMs (Medtronic-Guardian, Dexcom-G6, and Abbott-Libre-2) have MARD values of < 10%, in contrast to two decades ago when the MARD used to be > 20%. In addition, the majority of the new CGMs do not require calibrations, and the latest CGMs last for 10-14 days. An implantable 6-months CGM by Eversense-3 is now approved in the USA and Europe. Recently, the FDA approved Libre 3 which provides real-time glucose values every minute. Even though it is approved as an iCGM it is not interoperable with automatic-insulin-delivery (AID) systems. The newer CGMs that are likely to be launched in the next few months in the USA include the 10-11 days Dexcom G7 (60% smaller than the existing G6), and the 7-days Medtronic Guardian 4. Most of the newer CGM have several features like automatic initialization, easy insertion, predictive alarms, and alerts. It has also been noticed that an arm insertion site might have better accuracy than abdomen or other sites, like the buttock for kids. Lag time between YSI and different sensors have been reported differently, sometimes it is down to 2-3 min; however, in many instances, it is still 15-20 min, especially when the rate of change of glucose is > 2 mg/min. We believe that in the next decade there will be a significant increase in the number of people who use CGM for their day-to-day diabetes care.

14. Children and Adolescents: Standards of Care in Diabetes-2023

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment

Diabetes is a heterogeneous group of diseases that share a common trait of elevated blood glucose levels. Insulin lowers this level by promoting glucose utilization, thus avoiding short- and long-term organ damage due to the elevated blood glucose level. A patient with diabetes uses an insulin pump to dose insulin. The pump uses a controller to compute and dose the correct amount of insulin to keep blood glucose levels in a safe range. Insulin-pump controller development is an ongoing process aiming at fully closed-loop control. Controllers entering the market must be evaluated for safety. We propose an evaluation method that exploits an FDA-approved diabetic patient simulator. The method evaluates a Cartesian product of individual insulin-pump parameters with a fine degree of granularity. As this is a computationally intensive task, the simulator executes on a distributed cluster. We identify safe and risky combinations of insulin-pump parameter settings by applying the binomial model and decision tree to this product. As a result, we obtain a tool for insulin-pump settings and controller safety assessment. In this paper, we demonstrate the tool with the Low-Glucose Suspend and OpenAPS controllers. For average ± standard deviation, LGS and OpenAPS exhibited 1.7 ± 0.6% and 3.2 ± 1.8% of local extrema (i.e., good insulin-pump settings) out of all the entire Cartesian products, respectively. A continuous region around the best-discovered settings (i.e., the global extremum) of the insulin-pump settings spread across 4.0 ± 1.1% and 4.1 ± 1.3% of the Cartesian products, respectively.

Mitigation of Rebound Hyperglycemia With Real-Time Continuous Glucose Monitoring Data and Predictive Alerts

BACKGROUND

Excess carbohydrate intake during hypoglycemia can lead to rebound hyperglycemia (RH). We investigated associations between RH and use of real-time continuous glucose monitoring (rtCGM) and an rtCGM system's predictive alert.

METHODS

RH events were series of sensor glucose values (SGVs) >180 mg/dL starting within two hours of an antecedent SGV <70 mg/dL. Events were characterized by their frequency, duration (consecutive SGVs >180 mg/dL × five minutes), and severity (area under the glucose concentration-time curve). To assess the impact of rtCGM, data gathered during the four-week baseline phase (without rtCGM) and four-week follow-up phase (with rtCGM) from 75 participants in the HypoDE clinical trial (NCT02671968) of hypoglycemia-unaware individuals were compared. To assess the impact of predictive alerts, we identified a convenience sample of 24 518 users of an rtCGM system without predictive alerts who transitioned to a system whose predictive alert signals an SGV ≤55 mg/dL within 20 minutes (Dexcom G5 and G6, respectively). RH events from periods of blinded versus unblinded rtCGM wear and from periods of G5 and G6 wear were compared with paired t tests.

RESULTS

Compared to RH events in the HypoDE baseline phase, the mean frequency, duration, and severity of events fell by 14%, 12%, and 23%, respectively, in the follow-up phase (all P < .05). Compared to RH events during G5 use, the mean frequency, duration, and severity of events fell by 7%, 8%, and 13%, respectively, during G6 use (all P < .001).

CONCLUSIONS

Rebound hypreglycemia can be objectively quantified and mitigated with rtCGM and rtCGM-based predictive alerts.

14. Children and Adolescents: Standards of Medical Care in Diabetes-2022

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Does Current Diabetes Technology Improve Metabolic Control? A Cross-Sectional Study on the Use of Insulin Pumps and Continuous Glucose Monitoring Devices in a Nationwide Pediatric Population

OBJECTIVE

To examine the use of multiple daily injections (MDI), insulin pumps, self-measured blood glucose (SMBG), and continuous glucose monitoring (CGM) systems, and their association with glycated hemoglobin (HbA1c), diabetic ketoacidosis (DKA), and severe hypoglycemia.

METHODS

In a pediatric population-based nationwide cross-sectional study, we analyzed data from 2623 participants up to 18 years of age with type 1 diabetes, using 2017 annual data from the Norwegian Childhood Diabetes Registry. HbA1c was adjusted for age, gender, and diabetes duration. Using a linear mixed-effects model, we assessed HbA1c and the incidence of DKA and severe hypoglycemia according to the use of MDI, insulin pumps, SMBG, and CGM.

RESULTS

We observed that 74.7% of participants were using an insulin pump and 52.6% were using a CGM system. Mean HbA1c was 7.8% (62 mmol/mol). The HbA1c of pump users was 0.14 percentage points (pp) higher than that of MDI users. Fewer pump users than MDI users achieved an HbA1c of < 7.5% (38.3 vs. 41.6%). CGM users had a 0.18 pp lower HbA1c than SMBG users, with 40.5 and 38.0%, respectively, achieving an HbA1c of < 7.5%. The incidence of severe hypoglycemia or hospitalization due to DKA was not different in pump and CGM users compared with nonusers. Compared with other insulin pumps, patch pump use was associated with a significantly lower odds ratio for DKA.

CONCLUSIONS

Despite the broad use of diabetes technology, as many as 61% of our pediatric cohort did not reach the HbA1c target recommended by the International Society for Pediatric and Adolescent Diabetes (ISPAD). Lower HbA1c was associated with CGM use but not with insulin pump use. Acute complications were not less frequent in the groups using insulin pumps or CGM compared with those using MDI and SMBG. Further research is required to explore the lower incidence of DKA among patch pump users.

TRIAL REGISTRATION

ClinicalTrials.gov identifier NCT04201171.

Predictive Low-Glucose Suspend Necessitates Less Carbohydrate Supplementation to Rescue Hypoglycemia: Need to Revisit Current Hypoglycemia Treatment Guidelines

Current guidelines recommend 15-20 g of carbohydrate (CHO) for treatment of mild to moderate hypoglycemia. However, these guidelines do not account for reduced insulin during suspensions with predictive low-glucose suspend (PLGS). We assessed insulin suspensions, hypoglycemic events, and CHO treatment during a 20-h inpatient evaluation of an investigational system with a PLGS feature, including an overnight basal up-titration period to activate the PLGS. Among 10 adults with type 1 diabetes, there were 59 suspensions; 7 suspensions were associated with rescue CHO and 5 with hypoglycemia. Rescue treatment consisted of median 9 g CHO (range: 5-16 g), with no events requiring repeat CHO. No rescue CHO were given during or after insulin suspension for the overnight basal up-titration. To minimize rebound hyperglycemia and needless calorie intake from hypoglycemia overtreatment, updated guidance for PLGS systems should reflect possible need to reduce CHO amounts for hypoglycemia rescue associated with an insulin suspension. The clinical trial was registered with ClinicalTrials.gov (NCT03890003).

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

OBJECTIVE

To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders.

METHODS

The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development.

MAIN OUTCOME MEASURES

Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range.

RESULTS

This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base.

RECOMMENDATIONS

Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology.

CONCLUSIONS

Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.

13. Children and Adolescents: Standards of Medical Care in Diabetes-2021

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Diabetes Management During Breastfeeding in Women with Type 1 Diabetes

PURPOSE OF REVIEW

To provide an update on glycemic management of type 1 diabetes during breastfeeding with focus on diet and insulin treatment to prevent hypoglycemia, ketoacidosis, and weight retention. Recommendations for insulin pump settings are given.

RECENT FINDINGS

Women with type 1 diabetes are encouraged to breastfeed. Hypoglycemia is a concern in the breastfeeding period among women with type 1 diabetes, and ketoacidosis may also occur. The usual goals for glucose values for persons with diabetes also apply during breastfeeding. The recommended minimum daily carbohydrate intake is 210 g during breastfeeding, and this may contribute to prevention of hypoglycemia and ketoacidosis while aiming for gradual weight loss. Insulin requirements are 21% lower during breastfeeding than before pregnancy. Diabetes management in breastfeeding women with type 1 diabetes includes the same goals for glucose values as in other persons with diabetes, sufficient carbohydrate intake, and adequate reduction in insulin dose.

Diabetes Technology Use in Adults with Type 1 and Type 2 Diabetes

In the last 2 decades, diabetes technology has emerged as a branch of diabetes management thanks to the advent of continuous glucose monitoring (CGM) and increased availability of continuous subcutaneous insulin infusion systems, or insulin pumps. These tools have progressed from rudimentary instruments to sophisticated therapeutic options for advanced diabetes management. This article discusses the available CGM and insulin pump systems and the clinical benefits of their use in adults with type 1 diabetes, intensively insulin-treated type 2 diabetes, and pregnant patients with preexisting diabetes.

13. Children and Adolescents: Standards of Medical Care in Diabetes-2020

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc20-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc20-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

One Year Clinical Experience of the First Commercial Hybrid Closed-Loop System

OBJECTIVE

In September 2016, the U.S. Food and Drug Administration approved the Medtronic 670G "hybrid" closed-loop system. In Auto Mode, this system automatically controls basal insulin delivery based on continuous glucose monitoring data but requires users to enter carbohydrates and blood glucose for boluses. To track real-world experience with this first commercial closed-loop device, we prospectively followed pediatric and adult patients starting the 670G system.

RESEARCH DESIGN AND METHODS

This was a 1-year prospective observational study of patients with type 1 diabetes starting the 670G system between May 2017 and May 2018 in clinic.

RESULTS

Of the total of 84 patients who received 670G and consented, 5 never returned for follow-up, with 79 (aged 9-61 years) providing data at 1 week and 3, 6, 9, and/or 12 months after Auto Mode initiation. For the 86% (68 out of 79) with 1-week data, 99% (67 out of 68) successfully started. By 3 months, at least 28% (22 out of 79) had stopped using Auto Mode; at 6 months, 34% (27 out of 79); at 9 months, 35% (28 out of 79); and by 12 months, 33% (26 out of 79). The primary reason for continuing Auto Mode was desire for increased time in range. Reasons for discontinuation included sensor issues in 62% (16 out of 26), problems obtaining supplies in 12% (3 out of 26), hypoglycemia fear in 12% (3 out of 26), multiple daily injection preference in 8% (2 out of 26), and sports in 8% (2 out of 26). At all visits, there was a significant correlation between hemoglobin A1c (HbA1c) and Auto Mode utilization.

CONCLUSIONS

While Auto Mode utilization correlates with improved glycemic control, a focus on usability and human factors is necessary to ensure use of Auto Mode. Alarms and sensor calibration are a major patient concern, which future technology should alleviate.

Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications

By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.

Impact of sensor-augmented pump therapy with predictive low-glucose management on hypoglycemia and glycemic control in patients with type 1 diabetes mellitus: 1-year follow-up

AIMS

To describe real-life experience with sensor-augmented pump therapy with predictive low-glucose management (SAPT-PLGM), in terms of hypoglycemia and glycemic control after one year of follow-up in T1D patients with hypoglycemia as the main indication of therapy.

METHODS

Retrospective cohort study under real life conditions. Baseline and one-year follow-up variables of glycemic control, hypoglycemia and glycemic variability were compared.

RESULTS

Fifty patients were included, 31 on prior treatment with SAPT with low-glucose suspend (LGS) feature and 19 on multiple dose insulin injections (MDI). Mean HbA1c decreased in the MDI group (8.24%-7.08%; p = 0.0001). HbA1c change was not significant in the SAPT-LGS group. Area under the curve (AUC) below 70 mg/dl improved in both SAPT-LGS and MDI groups while AUC, %time and events below 54 mg/dl decreased in SAPT-LGS group. Glycemic variability improved in the MDI group. Less patients presented severe hypoglycemia with SAPT-PLGM in both groups, however the change was non-significant.

CONCLUSIONS

Under real life conditions, SAPT-PLGM reduced metrics of hypoglycemia in patients previously treaded with MDI and SAPT-LGS without deteriorating glycemic control in SAPT-LGS patients, while improving it in patients treated with MDI.

Improving pregnancy outcomes in women with diabetes mellitus: modern management

Women with pre-existing (type 1 or type 2) diabetes mellitus are at increased risk of pregnancy complications, such as congenital malformations, preeclampsia and preterm delivery, compared with women who do not have diabetes mellitus. Approximately half of pregnancies in women with pre-existing diabetes mellitus are complicated by fetal overgrowth, which results in infants who are overweight at birth and at risk of birth trauma and, later in life, the metabolic syndrome, cardiovascular disease and type 2 diabetes mellitus. Strict glycaemic control with appropriate diet, use of insulin and, if necessary, antihypertensive treatment is the cornerstone of diabetes mellitus management to prevent pregnancy complications. New technology for managing diabetes mellitus is evolving and is changing the management of these conditions in pregnancy. For instance, in Europe, most women with pre-existing diabetes mellitus are treated with insulin analogues before and during pregnancy. Furthermore, many women are on insulin pumps during pregnancy, and the use of continuous glucose monitoring is becoming more frequent. In addition, smartphone application technology is a promising educational tool for pregnant women with diabetes mellitus and their caregivers. This Review covers how modern diabetes mellitus management with appropriate diet, insulin and antihypertensive treatment in patients with pre-existing diabetes mellitus can contribute to reducing the risk of pregnancy complications such as congenital malformations, fetal overgrowth, preeclampsia and preterm delivery.

Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning

Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia. Therefore, hypoglycaemia is the main safety problem in the treatment of type 1 diabetes, negatively affecting the quality of life of patients suffering from this disease. Decision support tools based on machine learning methods have become a viable way to enhance patient safety by anticipating adverse glycaemic events. This study proposes the application of four machine learning algorithms to tackle the problem of safety in diabetes management: (1) grammatical evolution for the mid-term continuous prediction of blood glucose levels, (2) support vector machines to predict hypoglycaemic events during postprandial periods, (3) artificial neural networks to predict hypoglycaemic episodes overnight, and (4) data mining to profile diabetes management scenarios. The proposal consists of the combination of prediction and classification capabilities of the implemented approaches. The resulting system significantly reduces the number of episodes of hypoglycaemia, improving safety and providing patients with greater confidence in decision-making.

Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review

For individuals affected by Type 1 diabetes (T1D), a chronic disease in which the pancreas does not produce any insulin, maintaining the blood glucose (BG) concentration as much as possible within the safety range (70–180 mg/dl) allows avoiding short- and long-term complications. The tuning of exogenous insulin infusion can be difficult, especially because of the inter- and intra-day variability of physiological and behavioral factors. Continuous glucose monitoring (CGM) sensors, which monitor glucose concentration in the subcutaneous tissue almost continuously, allowed improving the detection of critical hypo- and hyper-glycemic episodes. Moreover, their integration with insulin pumps for continuous subcutaneous insulin infusion allowed developing algorithms that automatically tune insulin dosing based on CGM measurements in order to mitigate the incidence of critical episodes. In this work, we aim at reviewing the literature on methods for CGM-based automatic attenuation or suspension of basal insulin with a focus on algorithms, their implementation in commercial devices and clinical evidence of their effectiveness and safety.

13. Children and Adolescents: Standards of Medical Care in Diabetes-2019

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

In-Clinic Evaluation of the MiniMed 670G System "Suspend Before Low" Feature in Children with Type 1 Diabetes

BACKGROUND

The Medtronic predictive low-glucose management (PLGM) algorithm automatically stops insulin delivery when sensor glucose (SG) is predicted to reach or fall below a preset low-glucose value within the next 30 min, and resumes delivery after hypoglycemia recovery. The present study evaluated the PLGM algorithm performance of the MiniMed™ 670G system SmartGuard™ "suspend before low" feature in children aged 7-13 years with type 1 diabetes (T1D).

METHOD

Participants (N = 105, mean ± standard deviation of 10.8 ± 1.8 years) underwent an overnight in-clinic evaluation of the "suspend before low" feature with a preset low limit of 65 mg/dL. After exercise, frequent sample testing (FST) was conducted every 5 min if values were <70 mg/dL; every 15 min if 70-80 mg/dL; and every 30 min if >80 mg/dL. First-day performance of the Guardian™ Sensor 3 glucose sensor and continuous glucose monitoring system was also evaluated.

RESULTS

Activation of the "suspend before low" feature occurred in 79 of the 105 participants, 79.7% (63/79) did not result in SG falling below 65 mg/dL. Mean glucose at activation was 102 ± 19 mg/dL and the initial insulin suspension duration was 87.5 ± 32.7 min. Four hours after insulin resumption, mean reference glucose was 130 ± 42 mg/dL. Mean absolute relative difference between the FST reference glucose and SG values on the first day of sensor wear was 11.4%. For the 26 participants in whom the "suspend before low" feature did not activate, none involved a reference glucose value ≤65 mg/dL, suggesting that the PLGM algorithm performed as intended.

CONCLUSION

In children aged 7-13 years with T1D, the "suspend before low" feature of the MiniMed 670G system demonstrated a hypoglycemia prevention rate of nearly 80% after exercise and did not involve rebound hyperglycemia. There were no events of severe hypoglycemia during the evaluation.

Accuracy of a Fourth-Generation Continuous Glucose Monitoring System in Children and Adolescents with Type 1 Diabetes

BACKGROUND

This study evaluated the safety and performance of the Guardian™ continuous glucose monitoring (CGM) system in children and adolescents with type 1 diabetes (T1D).

MATERIALS AND METHODS

Subjects 2-18 years of age (mean ± standard deviation [SD] 13.1 ± 3.9 years) with T1D and duration of diagnosis ≥1 year were enrolled at 11 sites in the United States and wore two Guardian Sensor 3 sensors in the abdomen and/or buttock. Sensors were connected to a transmitter paired with either a Guardian Connect system (i.e., mobile device with software application allowing display of sensor glucose [SG] values) or a Guardian Link 3 transmitter used as a Glucose Sensor Recorder (GSR). There were 145 participants who underwent a 6-h in-clinic frequent sample testing (FST) on day 1 (n = 54), day 3 (n = 48), or day 7 (n = 43) postsensor insertion. During FST, SG values were compared with a Yellow Springs Instrument (YSI) plasma reference every 5-15 min (n = 124, 7-18 years of age; n = 2, 2-6 years of age), or to a self-monitoring of blood glucose (SMBG) reference every 5-30 min (n = 19, 2-6 years of age).

RESULTS

The overall mean absolute relative difference (ARD ± SD) between SG and reference values (YSI or SMBG) when calibrating approximately every 12 h, was 10.9% ± 10.7% (3102 paired points) for sensors communicating with the Guardian Connect system and 11.1% ± 10.6% (2624 paired points) for sensors connected to the GSR. The overall percentage of SG values within ±20% of reference values >80 mg/dL or within 20 mg/dL of reference values ≤80 mg/dL was 87.8% for the Guardian Connect system and 86.7% for the GSR, respectively. There was one device-related adverse event of contact dermatitis, but no serious device-related adverse events.

CONCLUSIONS

The Guardian CGM system demonstrated good accuracy in children and adolescents. These findings support its use in sensor-integrated insulin pump platforms, as well as a standalone technology, for managing diabetes in pediatric populations.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

12. Children and Adolescents: Standards of Medical Care in Diabetes-2018

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Continuous glucose monitoring: A review of the technology and clinical use

Continuous glucose monitoring (CGM) is an increasingly adopted technology for insulin-requiring patients that provides insights into glycemic fluctuations. CGM can assist patients in managing their diabetes with lifestyle and medication adjustments. This article provides an overview of the technical and clinical features of CGM based on a review of articles in PubMed on CGM from 1999 through January 31, 2017. A detailed description is presented of three professional (retrospective), three personal (real-time) continuous glucose monitors, and three sensor integrated pumps (consisting of a sensor and pump that communicate with each other to determine an optimal insulin dose and adjust the delivery of insulin) that are currently available in United States. We have reviewed outpatient CGM outcomes, focusing on hemoglobin A1c (A1C), hypoglycemia, and quality of life. Issues affecting accuracy, detection of glycemic variability, strategies for optimal use, as well as cybersecurity and future directions for sensor design and use are discussed. In conclusion, CGM is an important tool for monitoring diabetes that has been shown to improve outcomes in patients with type 1 diabetes mellitus. Given currently available data and technological developments, we believe that with appropriate patient education, CGM can also be considered for other patient populations.

Pharmacotherapy options for pediatric diabetes

PURPOSE OF REVIEW

Type 1 diabetes (T1D) and type 2 diabetes (T2D) are frequent conditions during childhood and adolescence. The present review offers an update on current available treatment strategies for T1D and T2D approved for use in children and adolescents.

RECENT FINDINGS

Insulin remains the main and essential therapeutic strategy in youth with T1D. A second generation of insulin analogues is being evaluated and could help in improving glycemic control. Over the last decades advances in technology have allowed the implementation of insulin pump therapy and continuous glucose monitoring, and are now leading the way towards the development of an artificial pancreas or closed loop system.Treatment of T2D is based on lifestyle interventions and metformin as the first-line drug to be used. Little evidence is available for other oral hypoglycemic drugs, currently used in adults.

SUMMARY

Although much progress has been made in the field of diabetes management, there are still several unmet goals. One of the main issues is to develop a system allowing more physiological insulin coverage. For both T1D and T2D, there is a strong need of new drugs to be used alone or in combination, mainly in patients struggling to achieve good glycemic control.

Epigenetic Manipulation Facilitates the Generation of Skeletal Muscle Cells from Pluripotent Stem Cells

Human pluripotent stem cells (hPSCs) have the capacity to differentiate into essentially all cell types in the body. Such differentiation can be directed to specific cell types by appropriate cell culture conditions or overexpressing lineage-defining transcription factors (TFs). Especially, for the activation of myogenic program, early studies have shown the effectiveness of enforced expression of TFs associated with myogenic differentiation, such as PAX7 and MYOD1. However, the efficiency of direct differentiation was rather low, most likely due to chromatin features unique to hPSCs, which hinder the access of TFs to genes involved in muscle differentiation. Indeed, recent studies have demonstrated that ectopic expression of epigenetic-modifying factors such as a histone demethylase and an ATP-dependent remodeling factor significantly enhances myogenic differentiation from hPSCs. In this article, we review the recent progress for in vitro generation of skeletal muscles from hPSCs through forced epigenetic and transcriptional manipulation.